پیش‌بینی مناطق مستعد وقوع سیل با استفاده از مدل‌های پیشرفته یادگیری ماشین ( دشت بیرجند)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد، گروه مهندسی نقشه‌برداری، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران.

2 کارشناس ارشد مهندسی عمران آب و سازه‌های هیدرولیکی، عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.

3 دانشیار، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران.

4 دانش‌آموخته کارشناسی ارشد، گروه آب و سازه‌های هیدرولیکی، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.

5 استادیار، گروه معدن، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران.

چکیده

تحقیقات در مورد مدل‌های پیش‌بینی سیل، یکی از اقدامات اولیه در کاهش خسارت سیل و مدیریت سیل‌های آینده در حوضه‌های آبریز است. هدف از مطالعه حاضر، ارزیابی حساسیت سیل در حوضه آبریز دشت بیرجند از طریق چهار مدل یادگیری ماشین شامل ماشین بردار پشتیبان (‏SVM)‏، درخت تصمیم J48‏، جنگل تصادفی (‏RF) و سیستم‌ استنتاج عصبی فازی (ANFIS) است. لذا جهت پیاده‌سازی و اعتبارسنجی مدل‌های ذکر شده، فهرستی از مناطق مستعد سیل در منطقه مورد مطالعه تهیه شد (42 موقعیت سیل‌خیز). علاوه بر این، 19 معیار هیدروژئولوژیکی، توپوگرافی، زمین‌شناسی و محیطی مؤثر بر وقوع سیل در منطقه مورمطالعه استخراج شدند تا برای پیش‌بینی نقشه حساسیت سیل مورد استفاده قرار گیرند. نتایج نشان داد که بالاترین دقت مربوط به مدل RF (845/0) و کمترین دقت مربوط به مدل SVM‏ (‏791/0)‏ بود. علاوه بر این، اعتبارسنجی نتایج با استفاده از منحنی ROC نشان داد که دقیق‌ترین مقادیر حساسیت سیل نیز به مدل RF اختصاص دارد (958/0AUC=)‏. نتایج این مطالعه می‌تواند به منظور مدیریت مناطق آسیب‌پذیر و کاهش خسارات سیل استفاده گردد.

کلیدواژه‌ها

عنوان مقاله [English]

Predicting flood prone areas using advanced machine learning models (Birjand plain)

نویسندگان [English]

  • Seyed Ahmad Eslaminezhad 1
  • Mobin Eftekhari 2
  • Mohammad Akbari 3
  • Ali Haji Elyasi 4
  • Hadi Farhadian 5
1 M.Sc. Graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran.
2 M.Sc., Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3 Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
4 M.Sc. Graduate, Department of Water and Hydraulic Structure, K. N. Toosi University of Technology, Tehran, Iran.
5 Assistant Professor, Department of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
چکیده [English]

Research on flood predicting models is one of the first steps in reducing flood damage and managing future floods in catchments. The aim of this study was to evaluate flood susceptibility in Birjand plain catchment through four machine learning models including support vector machine (SVM), J48 decision tree, random forest (RF) and Adaptive neuro fuzzy inference system (ANFIS). Therefore, in order to implement and validate the mentioned models, a list of flood-prone areas in the study area was prepared (42 flood-prone locations). In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood occurrence in the study area were extracted to be used to predict flood susceptibility map. The results showed that the highest accuracy was related to the RF model (0.845) and the lowest accuracy was related to the SVM model (0.791). In addition, the validation of the results using the ROC curve showed that the most accurate values of flood susceptibility belong to the RF model (AUC = 0.958). The results of this study can be used to manage vulnerable areas and reduce flood damage.

کلیدواژه‌ها [English]

  • Birjand plain
  • flood susceptibility
  • Geospatial information system (GIS)
  • machine learning

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مدیریت آب و آبیاری
دوره 11، شماره 4
بهمن 1400
صفحه 885-904

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